Super-resolution microscopy techniques permit exceedingly high resolution imaging of specimens using optical radiation. The resolution can be finer than the wavelength of the optical radiation used for imaging. Such techniques include including stimulated emission depletion (STED) and RESOLFT microscopy (see e.g. Hell S W. Nat. Biotech. 2003; 21:1347; and Hell S W. Science. 2007; 316:1153.), saturated structured illumination microscopy (SSIM) (see e.g. Gustafsson M G L. Proc. Natl. Acad. Sci., USA. 2005; 102:13081), stochastic optical reconstruction microscopy (STORM) (see e.g. Rust M J, Bates M, Zhuang X. Nat. Meth. 2006; 3:793; Bates M, Huang B, Dempsey G T, Zhuang X. Science. 2007; 317:1749; and Huang, Bo “Three-dimensional Super-resolution Imaging by Stochastic Optical Reconstruction Microscopy” Science 319 (5864): 810-813 (2008)), photoactivated localization microscopy (PALM) (see e.g. Betzig E, et al. Science. 2006; 313:1642; and Hess S T, Girirajan T P K, Mason M D. Biophys. J. 2006; 91:4258) and other methods using similar principles (see e.g. Sharonov A, Hochstrasser R M. Proc. Natl. Acad. Sci., USA. 2006; 103:18911; Egner A, et al. Biophys. J. 2007; 93:3285; and Bock H, et al. Appl. Phys. B. 2007; 88:161). Such techniques can achieve lateral resolutions of better than 50 nm. Many of these techniques are single-molecule-localization (SML) techniques which create conditions in which light is emitted from single molecules. The knowledge that light emissions occur at discrete locations can be used to create exceptionally high resolution images.
STORM uses photo-switchable fluorescent probes to temporally separate the otherwise spatially overlapping images of individual molecules, allowing for precise localization of individual fluorescent labels in the sample. Although three dimensional (3D) STORM based on astigmatic single-molecule localizations has been gaining popularity, high accuracy deep imaging still faces many challenges. In STORM microscopy, an acquisition time of several minutes is often needed to accumulate a sufficient number of fluorophore positions and construct an informative image.
Sample drift is movement of a sample being imaged relative to imaging apparatus (e.g. relative to an objective lens and imaging sensor such as a camera). Sample drift compromises the precision and accuracy of imaging. Sample drift can occur in all three dimensions, and arises from a wide number of sources including mechanical vibrations and other mechanical movements, temperature changes, temperature gradients and the like.
Although the lateral accuracy of fluorophore localization by super-resolution imaging techniques such as STORM can be better than 10 nm, sample drift due to thermal gradients or mechanical motions can easily be in the hundreds of nanometers. In conventional fluorescence microscopy typical resolutions are on the order of 300 nm or more and sample drifts of 100 nm may be tolerable. In super-resolution microscopy a sample drift of 100 nm or more during acquisition may destroy the high resolution nature of the image. Therefore minimizing sample drift often becomes the most important factor in determining the performance of a super-resolution microscope.
Furthermore, the above techniques can require relatively long data acquisition times (e.g. times on the order of several minutes or more are not uncommon). Such long data acquisition place even higher demands on minimizing sample drift. SML methods may routinely take minutes to hours to obtain a single image. In SML-based super-resolution methods, minimizing sample drift over long periods of time is desirable.
Various approaches to correcting for positional drift have been described in the literature. These include online correction using fiducial markers (see e.g. Pertsinidis, A., Zhang, Y. & Chu, S. Nature 466, 647-651 (2010) and Carter, A. R. et al. Appl. Opt. 46, 421-427 (2007)) as well as offline processing algorithms using a bright-field image (see e.g. Mennella, V. et al. Nat. Cell Biol. 14, 1159-1168 (2012)) or consecutive blink tracking (see e.g. Geisler, C. et al. Opt. Express 20 (2012)). Other references describing image stabilization or drift correction techniques include:                Ashley R. Carter et al: “Stabilization of an optical microscope to 0.1 nm in three dimensions”, APPLIED OPTICS, vol. 46, no. 3, 1 Jan. 2007 (2007 Jan. 1), page 421        WO2013063096A1 Multifunction autofocus system and method for automated microscopy;        US20130070339 Method and device for image stabilization in an optical observation or measurement instrument;        US20050141081 Method for correcting drift in an optical device; and        U.S. Pat. No. 7,928,409 Real-time, active picometer-scale alignment, stabilization, and registration in one or more dimensions.        
Offline processing algorithms have difficulty in correcting for large drifts. Real-time drift correction techniques using fiducial markers have so-far produced the best super-resolution images. Fiducial markers, typically fluorescent beads affixed to a coverslip, are used as reference points to measure and correct for drift. The positions of bright fiducial markers can typically be determined within an error of a few nm. However, when the objective lens is focused at a depth into the sample greater than the depth of field provided by the objective lens (typically on the order of 0.5 μm or less), the fiducial markers on the coverslip are out of focus making it difficult or impossible to obtain accurate measurements of drift.
There is a need for practical and cost-effective ways to compensate for sample drift in super-resolution microscopy.